The automotive diagnostic industry has been using data mining techniques to develop expert suggestions and identify effective fixes for vehicle problems. Data related to maintenance activities is collected from garages located all over the country. A group of human experts or expert computer systems then review and analyze the collected data for the purpose of identifying and validating effective fixes, and generating expert suggestions. The effective fixes and expert suggestions are then implemented in diagnostic software or incorporated in user's manuals to assist technicians in performing diagnoses on vehicles.
The process of collecting diagnostic data is often tedious and requires a lot of human work and intervention. Some garages require technicians to write down steps and services that they perform on each vehicle. A clerk then reviews, compiles and enter the data into a computer system for transmission to a remote data depository such that data mining can be performed. However, not all garages have the resources or capacity needed to collect and transmit diagnostic data. Some garages do not have enough manpower to compile and enter the diagnostic data, while others do not have the hardware or equipment to transmit the data. As a result, a lot of valuable data is unavailable for analysis due to difficulties in collecting or transmitting diagnostic data.
Therefore, there is a need to automate the process of collecting diagnostic data. There is another need to identify the occurrence or completion of a maintenance process and collect the diagnostic data. There is a further need to timely transmit the diagnostic data to a data depository.